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Nathan L. Brouwer Phd

Computational ecologist specializing in complex datasets

I am a computational ecologist, data scientist and educator with 15 years of experience extracting insights from messy data and communicating the results. I specialize in using generalized-linear mixed models (GLMMs) to analyze data from long-term observational studies. Since my PhD I have also taught myself key aspects of bioinformatics, phylogenetics, machine learning and population genomics.

Education

Seattle Pacific University

B.S. in Biology, with Honors

N/A

2002

Research & Professional Experience

Associate Teaching Professor - General & Quantitative Biology

Unv. of Pittsburgh Dept. of Biological Sciences

N/A

2019 - present

Post-doctoral Research Associate - Avian Conservation

National Aviary of Pittsburgh

N/A

2015 - 2019

  • GLMMS: Analyze decade-long tropical bird population & community datasets.
  • Data Cleaning: Clean & merge diverse datasets of environmental data & organism traits.
  • R packages: Implement models on migratory birds as reproducible software,
  • Computational Statistics: Develop sensitivity and uncertainty analyses methods for for migration models.

Adjunct Professor - Biology

La Roche College, Pittsburgh

N/A

2018-2019 (Fall & Winter)

  • Co-taught intro to research course (fall) & scientific writing (spring)
  • Developed new lab and data analysis activities

Adjunct Professor - Biological Data Analysis

Dusquesne Unv. & California Unv. of PA

N/A

2016-2017

  • R programming & data analysis: Teach graduate (Dusquesne, Spring 2017) & undergraduate data analysises courses (CalU, Fall 2016 & 2017)

PASSIONS

BAYESIAN STATISTICS: Bayesian approaches have always been apparent to me as the optimal way to approach complex ecological data. Having recently made the time to start using rstan, I’m excited to explore the possibilities of working directly in Stan.

MACHINE LEARNING: While working through Kaggle exercises this summer to prepare for my most recent Computational Biology class, the beauty and power of supervised ML methods were revealed to me.

REPRODUCIBLE WORKFLOWS

Graduate Research Assistant

Department of Biological Sciences, University of Pittsburgh

N/A

2010 - 2015

  • Data Cleaning: Update, clean and manage decade-long plant demographic experiment.
  • GLMMs: Determine appropriate model structures and analyze data.
  • Field work: Design & carry out research on plant demography.

Peace Corps Volunteer - Agroforestry Outreach

National Agricultural Research Institute, The Gambia, West Africa

N/A

2004 - 2006

  • Assist in staff development, including data analysis & experimental design,
  • Conducted outreach and training on agroforestry & sustainable agriculture

Infecious Disease Research Scientist

University of Washington Department of Allergy & Infectious Disease

N/A

2002 - 2004

  • Lab work: Conduct experiments on pathogen cell-adhesion proteins.

Publications - Science Education

A Little Book of R for Bioinformatics vs. 2.0

Open-access bioinformatics primer.

N/A

2022

Coghlan (au.) & Brouwer (ed., au)

Foundations of Biology and Environmental Science: An Open-Access Encyclopedia

Compilation of Open-Access resources on general biology, computational bioloyg, and environmental science.

N/A

N/A

Brouwer (ed., au.)

R Packages

Population Modeling: redstart: An R package for Periodic Full-Annual Cycle Avian Population Models and Monte-Carlo simulation.

R implementation & replication of Runge & Marra (2005) Modeling Seasonal Interactions in the Population Dynamics of Migratory Birds.

N/A

N/A

Brouwer et al.

  • Website & Tutorials: brouwern.github.io/FACavian/index.html

Publications

Population Models: Direct effects of a non-native invader erode native plant fitness in the forest understory

Journal of Ecology 108:189–198

N/A

2019

Bialic-Murphy, Brouwer & Kaliz.

Data & Code: Dryad

GAMM: Increased photosynthetic performance of an invasive forest herb mediated by deer overabundance.

AoB Plants 9: plx011

N/A

2017

Heberling, Brouwer & Kalisz.

GAMM Code: GitHub Data & Code:AoB Plants